Digital transformation (DT) directly influences organizational competitiveness, reshaping value creation and necessitating adaptation across industries. This study investigates how DT redefines strategic imperatives and the mechanisms of value creation within organizations, synthesizing findings from five thematic clusters: “Strategic Management in Digital Transformation of Organizations”, “Emerging Trends in Digital Entrepreneurship and Sustainability”, “Digital Capabilities and Business Model Innovation”, “Digitalization and Transformation of SMEs”, and “Value Creation through Innovation and Digital Transformation”. The analysis uncovers crucial insights, including the shift towards business-to-business (B2B) value-oriented sales, the role of big data analytics in collaborative innovation, and the integration of AI-driven business models across retail, logistics, and healthcare. Strategic alignment between technological advances and organizational goals emerges as essential, especially for SMEs facing resources, regulatory compliance, and skills development challenges. Despite these insights, significant gaps remain. Future research should delve into the underexplored area of cross-industry best practices, particularly how SMEs can leverage digital tools to enhance resilience and adaptability in market volatility. Further investigation into the long-term impacts of digital entrepreneurship on sustainability is recommended, including metrics for measuring social and environmental value creation. Additionally, digital leadership roles, such as Chief Digital Officers, warrant more in-depth examinations to identify how these leaders can navigate the complexities of DT and maximize value co-creation. This systematic literature review and bibliometric analysis aim to consolidate current knowledge, address critical gaps, and lay the groundwork for future studies that support resilient and sustainable growth in an increasingly digital economy.

This systematic review presents a critical analysis of advanced machine learning (ML) and deep learning (DL) approaches for predicting the remaining useful life (RUL) of electric vehicle (EV) batteries. Conducted in accordance with PRISMA guidelines and using a novel adaptation of the Downs and Black (D&B) scale, this study evaluates 89 research papers and provides insights into the evolving landscape of RUL estimation. Our analysis reveals an evolving landscape of methodological approaches, with different techniques showing distinct capabilities in capturing complex degradation patterns in EV batteries. While recent years have seen increased adoption of DL methods, the effectiveness of different approaches varies significantly based on application context and data characteristics. However, we also uncover critical challenges, including a lack of standardized evaluation metrics, prevalent overfitting problems, and limited dataset sizes, that hinder the field’s progress. To address these, we propose a comprehensive set of evaluation metrics and emphasize the need for larger and more diverse datasets. The review introduces an innovative clustering approach that provides a nuanced understanding of research trends and methodological gaps. In addition, we discuss the ethical implications of DL in RUL estimation, addressing concerns about privacy and algorithmic bias. By synthesizing current knowledge, identifying key research directions, and suggesting methodological improvements, this review serves as a central guide for researchers and practitioners in the rapidly evolving field of EV battery management. It not only contributes to the advancement of RUL estimation techniques but also sets a new standard for conducting systematic reviews in technology-driven fields, paving the way for more sustainable and efficient EV technologies.

Artificial Intelligence is increasingly integrated into consumers’ daily lives, making it essential to understand the future impact of this relationship. This systematic literature review aims to understand the influence of AI on consumer behavior, identifying future trends for brands. Using Web of Science research, following the PRISMA method, and employing VOSviewer software for cluster and keyword analysis, the results demonstrate that AI impacts consumer behavior and brands’, presenting five clusters that highlight: consumer experience, relationship, online challenges, functionality, and autonomous purchasing. The study contributes to current knowledge by verifying that the AI-consumer relationship is complex, focusing on five key aspects: convenience, effectiveness, trust, security, and personalization, which collectively influence purchase decisions and loyalty. This article also demonstrates that AI can facilitate strategic marketing decisions through segmentation and prediction capabilities, enabling personalization in an increasingly digital and e-commerce world. Marketers must consider AI as a consumer and expand their capabilities.

Incorporating technology into virtual education encourages educational institutions to demand a migration from the current learning management system towards an intelligent virtual educational system, seeking greater benefit by exploiting the data generated by students in their day-to-day activities. Therefore, the design of these intelligent systems must be performed from a new perspective, which will take advantage of the new analytical functions provided by technologies such as artificial intelligence, big data, educational data mining techniques, and web analytics. This paper focuses on primary education in developing countries, showing the design of an intelligent virtual educational system to improve the efficiency of primary education through recommendations based on reliable data. The intelligent system is formed of four subsystems: data warehousing, analytical data processing, monitoring process and recommender system for educational agents. To illustrate this, the paper contains two dashboards that analyze, respectively, the digital resources usage time and an aggregate profile of teachers’ digital skills, in order to infer new activities that improve efficiency. These intelligent virtual educational systems focus the teaching–learning process on new forms of interaction on an educational future oriented to personalized teaching for the students, and new evaluation and teaching processes for each professor.

Effective quality control is crucial in industrial manufacturing for influencing efficiency, product dependability, and customer contentment. In the constantly changing landscape of industrial production, conventional inspection methods may fall short, prompting the need for inventive approaches to enhance precision and productivity. In this study, we investigate the application of smart glasses for real-time quality inspection during assembly processes. Our key innovation involves combining smart glasses’ video feed with a server-based image recognition system, utilizing the advanced YOLOv8 model for accurate object detection. This integration seamlessly merges mixed reality (MR) with cutting-edge computer vision algorithms, offering immediate visual feedback and significantly enhancing defect detection in terms of both speed and accuracy. Carried out in a controlled environment, our research provides a thorough evaluation of the system’s functionality and identifies potential improvements. The findings highlight that MR significantly elevates the efficiency and reliability of traditional inspection methods. The synergy of MR and computer vision opens doors for future advancements in industrial quality control, paving the way for more streamlined and dependable manufacturing ecosystems.